Reinforcement learning–based adaptive strategies for climate change adaptation: An application for coastal flood risk management

《基于强化学习的自适应气候适应策略：纽约曼哈顿海岸洪水风险管理应用》

Kairui Feng, Ning Lin, et al.

• Reinforcement learning reduces coastal flood adaptation costs by up to 77%. 强化学习可将沿海洪水适应成本最多降低77%。

• RL integrates protection, accommodation, and retreat strategies for higher efficiency. 强化学习整合防护、适应与退避策略，提升经济效益。

• RL effectively controls tail risks and adapts to climate uncertainties. 强化学习有效控制尾部风险，适应气候不确定性。

Hurricane Ida’s blackout-heatwave compound risk in a changing climate

《气候变化背景下飓风“艾达”引发的停电-热浪复合风险评估》

Kairui Feng, Ning Lin, et al.

• Return period of Ida-like compound events drops from 278 to 16.2 years under SSP5-8.5. “艾达级”复合灾害重现期将从278年降至16.2年（SSP5-8.5）

• Heatwave intensification is the dominant driver, increasing risk by 3–5 times. 热浪增强是风险上升的主要驱动因素，可使风险提升3–5倍

• Study informs grid resilience and adaptation strategies, such as levee upgrades and distributed energy systems 研究为电网韧性提升与气候适应策略（如加高海堤、分布式能源）提供量化依据

Tropical cyclone-blackout-heatwave compound hazard resilience in a changing climate

《气候变化下台风-停电-热浪复合灾害韧性研究》

Kairui Feng, Min Ouyang, Ning Lin

• Developed a pioneering model to project power outage resilience under evolving climate conditions. 构建了创新模型，预测气候变化背景下电网停电韧性。

• Proposed a cost-effective strategy to strengthen power systems against compound climate risks. 提出低成本强化电力系统应对复合气候灾害的策略。

• Featured by NSF, The Hill, Yahoo, Mirage, Vox, etc.

RainNet: A large-scale imagery dataset and benchmark for spatial precipitation downscaling

《RainNet：空间降尺度降水图像数据集与基准》

Xuanhong Chen, Kairui Feng (co-first), et al.

• Compiled a comprehensive real-world precipitation dataset (1979–2018). 收集并整理覆盖1979–2018年的大规模实测降水数据集。

• Evaluated multiple downscaling methods to accurately model high-resolution rainfall patterns. 系统评估多种降尺度方法，提升高分辨率降水预测精度。

• Provided high-resolution (4km) rainfall dataset and benchmark tools. 公布高分辨率（4km）降水数据及评估基准。

Download 数据集: Google Drive | 代码仓库: Github.